Presentation on theme: "Growth Mixture Modeling Workgroup Heterogeneous trajectories of cognitive decline: Association with neuropathology Hayden K., Reed B., Chelune G., Manly."— Presentation transcript:
Growth Mixture Modeling Workgroup Heterogeneous trajectories of cognitive decline: Association with neuropathology Hayden K., Reed B., Chelune G., Manly J., Pietrzak R., Revell A., Yang F., Tommet D., Jones R. March 27, 2009
Hypotheses 1.Cognitive decline is not normal. This will be revealed by the detection of at least two, and we suspect three population sub-groups with qualitatively distinct trajectories of cognitive decline: 1)little/no change in cognition with aging, and this will be the largest (“normative”) group 2)intermediate change with aging 3)rapid decline with aging
Hypotheses 2.Group membership will be differentially associated with AD-type neuropathology
Methods Participants: Religious Orders Study (N=1,048) followed annually for up to 15 years with neuropsychological battery; a subset of n=345 had autopsy data. Main Outcomes: Global composite of neuropsychological function; among decedents, amyloid and tangle neuropathology (as defined by NIA/Reagan criteria and grouped by tertile). Statistical Approach: Random effects growth model with mixture component. Separate multinomial logistic regression of classes on neuropathology.
Growth Mixture Model Note: 1 and 2 are baseline and linear slope, respectively. 3 is a retest/learning effect. is regressed on age group dummies used to model non-linearity of trajectories over age using a piecewise approach. c implies latent classes. Individually varying time steps are used to model the regression of y on 2 are modeled as age at observation centered within age group.
Multinomial Regression Models Z = age at baseline, education, gender
Summary of Model Fit (GMM) Classes Log- Para- Average Class Prob- Extracted Likelihood Scaling* meters P-Value+ aBIC** abilities by Assignment*** < < Notes: * Scaling Correction Factor for MLR ** Sample-Size Adjusted BIC *** Average Latent Class Probabilities for Most Likely Latent Class Membership + P-value from -2*LL difference test with scaling correction factor, the Satorra-Bentler scaled (mean-adjusted) chi-square (Satorra, 2006).
slow moderate fast
Growth Trajectories by Age Group Note: Model-implied trajectories, net of retest/learning effect, are illustrated.
Years until half-SD decline in cognitive function Trajectory Group Years for 0.5sd 95% Conf. Interval LLUL Slow Moderate Rapid Effects shown are unweighted averages over all age groups (Table 1).
Amyloid Burden by Group
Tangle Burden by Group
Odds of Class Membership by level of Neuropathology Tertiles based on distribution of neuropathology in NIA/Reagan Neuropathologic Criteria for AD "intermediate" and "high" likelihood groups. Cell entries are odds ratios from multinomial logistic regression models. Covariate and fully adjusted models include adjustment for age at baseline assessment, sex, and level of education. Fully adjusted models include covariates and neuropathology. * P<0.05, **P<0.01, *** P<0.001
Summary of Results The largest proportion of older adults in this study did not experience significant cognitive decline, and average 15 years until any notable change in cognitive function was detected. Pathology does not perfectly account for membership in the 3 groups identifying trajectories of cognitive decline; however presence of tangles at death was strongly associated with the rapid declining group
Things to do for this paper 1.See how few repeated observations are necessary to correctly classify people in trajectory class with reasonable accuracy. 2.Sensitivity analysis on missing data in GLOBCOG (differential missingness might exaggerate decline profiles). 3.Multiple imputation for missing data in multinomial logistic regression models. 4.Write up in 2-2.5k words
Future Studies 1.Alternative class structures (Jones & Tommet) 2.Repeat in sub-domains: memory [episodic, semantic, working], perceptual speed, visuospatial